def run_rounds(self):
logger.info('Running rounds')
_run=0
while True:
os.system(clear_console)
logger.info('Evaluating Candidates.')
### YOU CAN OVERRIDE EVALUATE_CANDIDATE with your own Fitness Function
raw_scores = map(self.evaluate_candidate, self.candidates)# Sort candidates by score.
self.candidates.sort(key=lambda x: x.score, reverse=True)
# Update Top Scorers tally
self.top_five_scores.append(self.candidates[0:4]) #since we've already sorted.
self.top_scores.append(self.candidates[0].score)
#FOR DEBUG
# str_scores = ','.join(map(lambda x: str(x.score), self.candidates))
# logger.debug('Run #%s, Score_Summary=%s' % (_run, str_scores))
avg_score = sum(raw_scores, 0.0) / len(raw_scores)
self.avg_scores.append(avg_score)
logger.info('Run #%s, Average_Score=%s' % (_run, avg_score))
logger.info('Average scores over all last runs:: %s' % spark_string(self.avg_scores, True))
logger.info('Top score over all last runs:: %s' % spark_string(self.top_scores, True))
logger.info('Scores for this run:: %s' %spark_string(map(lambda x: x.score, self.candidates)))
if self.should_break(self.candidates, _run):
logger.info('=======================================')
logger.info('Scores Stabilized.')
logger.info('Summary Data: %s' % self.summary_data)
logger.info('=======================================')
logger.debug('Summary of each candidate in this pool:')
for candidate in self.candidates:
logger.debug('%s' % candidate.__unicode__())
logger.debug('=================================')
logger.info('Top Five Candidates + scores %s' % self.top_five_scores[-1])
logger.info('Best candidate DNA is: %s' % ''.join(self.top_five_scores[-1][0].DNA))
logger.info('=================================')
break
logger.debug('Culling pool')
self.cull_bad_candidates()
logger.debug('Generating new generation')
self.candidates = self.generate_new_pool()
_run = _run+1
def print_stats(stats):
for stat in stats:
stat_name = stat['key']
if stat_name == 'free':
continue
stat_values = stat['values']
stat_values.reverse()
#print '- %s: %s' % (stat_name, stat_values)
values = [v[1] for v in stat_values[:35]]
values.reverse()
#print '- %s:' % stat_name
print '%18s: %s' % (stat_name, spark_string(values))
for loglevel in ['FATAL', 'ERROR', 'WARN', 'INFO']:
results = []
for hour in range(-12, 1):
now = datetime.utcnow() + timedelta(hours=hour)
logging.debug(now.isoformat())
q = build_loglevel_query(loglevel, now)
post = requests.post('http://inw-48.rfiserve.net:9200/{0}/_search'.format(index), data=json.dumps(q))
logging.debug(pformat(post.request.url))
logging.info(pformat(post.json))
results.append(post.json['hits']['total'])
now = datetime.utcnow() - timedelta(minutes=5)
logging.debug(now.isoformat())
q = build_loglevel_query(loglevel, now, delta=timedelta(minutes=5))
post = requests.post('http://inw-48.rfiserve.net:9200/{0}/_search'.format(index), data=json.dumps(q))
logging.debug(pformat(post.request.url))
logging.info(pformat(post.json))
last_5 = post.json['hits']['total']
diff = map(lambda x: x[0]-x[1], zip(results[1:], results))
diff = ','.join(map(str, diff))
print u"{0}:\t{2} {1} ({3:.1f})({4:.1f}) {5}".format(loglevel, results[-1], spark.spark_string(results, fit_min=True), numpy.average(results), numpy.average(results)/12, last_5)
